Diva++: Dynamic Range Filtering over Hard Workloads

[2608.27616] Diva++: Dynamic Range Filtering over Hard Workloads Skip to main content Search Submit Donate Log in Computer Science > Data Structures and Algorithms arXiv:2608.27616 (cs) [Submitted on 27 Aug 2026] Title: Diva++: Dynamic Range Filtering over Hard Workloads Authors: Navid Eslami , Ioana O. Bercea , Niv Dayan View PDF HTML (experimental) Abstract: Range filters are compact probabilistic data structures that answer approximate range emptiness queries. They are used in many domains, e.g., in key-value stores, to quickly rule out the existence of keys in a given query range and avoid searching for them in storage. However, all existing range filters exhibit at least one of three shortcomings: (1) they do not provide any false positive rate or performance guarantees, (2) they do not support variable-length keys and query ranges, and (3) they do not allow dynamic updates. We introduce Diva, the first range filter to address all the above challenges simultaneously. Diva learns the dataset's distribution by sampling keys and storing them in a cache-efficient trie. It compresses keys in-between samples by removing their longest common prefix and truncating their suffixes while leaving enough bits in the middle (i.e., an infix) to differentiate the keys in sorted order. It stores infixes in constant-time dynamic data blocks, which it splits to handle insertions and expansions. It processes a range query by traversing the trie and checking for the inclusion of infixes in the target query range. We mathematically prove that Diva provides the best possible trade-off between memory and false positive rate on many common real-world data distributions. We extend these benefits to a wider range of real-world workloads by introducing Diva++, an enhanced Diva variant. Diva++ saves memory by removing redundancies among infixes using order-preserving entropy encoding. It then removes any remaining identical infixes and uses the freed space to store more bits of the original keys within compact binary tries. We compare Diva and Diva++ to all prior range filters, and show that they achieve a false positive rate on par with the state of the art on real-world datasets while supporting dynamicity and variable-length queries and keys. Comments: 31 pages. 19 figures. 6 tables. Submitted to the PVLDB Journal Subjects: Data Structures and Algorithms (cs.DS) ACM classes: E.1; F.2.2; H.2.8 Cite as: arXiv:2608.27616 [cs.DS] (or arXiv:2608.27616v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2608.27616 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Navid Eslami [ view email ] [v1] Thu, 27 Aug 2026 18:52:05 UTC (1,293 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.DS < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from (opens in new tab) (opens in new tab) (opens in new tab)

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